The homoskedasticity-only F-statistic and the heteroskedasticity-robust F-statistic typically are:

The homoskedasticity-only F-statistic and the heteroskedasticity-robust F-statistic typically are different.

Step-by-step explanation:

An F statistic is a value derived by running an ANOVA test or a regression analysis to find out if the means between two populations are significantly different.

The homoskedasticity-only” F-statistic is derived by running two regressions, one under the null hypothesis and one under the alternative hypothesis. If the “unrestricted” model fits sufficiently better, reject the null.

In the first regression, the restricted regression (the null hypothesis) is forced to be true. This is the regression in which all the coefficients are set to zero; the relevant regressors are excluded from the regression. In the second regression, the unrestricted regression, the alternative hypothesis is allowed to be true. If the sum of squared residuals is sufficiently smaller in the unrestricted than the restricted regression, then the test rejects the null hypothesis

The heteroskedasticity-robust F-statistic is built in to STATA (“test” command); this tests all q  restrictions at once.

The homoskedasticity-only F-statistic is important historically (and also in practice), and can  help intuition, but isn’t valid when there is heteroskedasticity

let x be the money she spent so that she can earn the free t-shirt.

since, she already spent $112.49

⇒

also, as per the statement: jennifer must spend $250 or more in order to qualify for a free t-shirt at the store.

then,

we have an inequality i.,e

subtract 112.49 from both sides we get;

therefore, $137. 51 more money does jennifer need to spend so that she can earn the free t-shirt